Efficient k-NN based HEp-2 cells classifier

Stoklasa, Roman; Majtner, Tomáš; Svoboda, David

doi:10.1016/j.patcog.2013.09.021

Cited by 39 publications

(21 citation statements)

References 41 publications

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“…The two-step approach employs the convex Bayesian functional and the convex region-scalable fitting energy functional, and requires only two subsequent steps. In the first step, authors use the region-scalable fitting energy functional, which can cope (Matula et al 2009), (Liao et al 2015), (Dogantekin, Avci, and Erkus 2013), (Stoklasa, Majtner, and Svoboda 2014), (Filipczuk, Krawczyk, and Woźniak 2013) Region-based segmentation These techniques operate iteratively by grouping together pixels which have similar values. The watershed transform is a region-based segmentation technique.…”

Section: Segmentationmentioning

confidence: 99%

“…(Liu and Liu 2013), , (Wang et al 2006), (Dogantekin, Avci, and Erkus 2013), (Ong and Chandran 2005), (Gertych et al 2015), (Abeysekera et al 2014), (Mao et al 2014), (dos Santos et al 2015), (Kayaaltı et al 2014), (Stoklasa, Majtner, and Svoboda 2014) Wavelet features.…”

Section: Texturementioning

confidence: 99%

“…In the research presented in (Stoklasa, Majtner, and Svoboda 2014) authors use k-nearest neighbor (k-NN) to classify Human Epithelial (HEp2) cells. The classifier is able to categorize pre-segmented images of HEp-2 cells into 6 classes.…”

Section: Classificationmentioning

confidence: 99%

“…The proposed classifier is k-NN based and was implemented using MESSIF framework (Batko et al 2008). In comparison with SVM, the advantages of k-NN based approach in the implicit support of multi-class division and (Dogantekin, Avci, and Erkus 2013), (Karkanis et al 2003), (Gertych et al 2015), (Ashcroft et al 2011), (Stoklasa, Majtner, and Svoboda 2014), (Nanni and Lumini 2007), (Filipczuk, Krawczyk, and Woźniak 2013) Non-probabilistic: support vector machine, K-nearest neighbor and linear discriminant analysis.…”

Section: Classificationmentioning

confidence: 99%

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Study of Image Analysis Algorithms for Segmentation, Feature Extraction and Classification of Cells

Gamarra

Zurek

San-Juan

2017

Journal of Information Systems Engineering & Management

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Recent advances in microcopy and improvements in image processing algorithms have allowed the development of computer-assisted analytical approaches in cell identification. Several applications could be mentioned in this field: Cellular phenotype identification, disease detection and treatment, identifying virus entry in cells and virus classification; these applications could help to complement the opinion of medical experts. Although many surveys have been presented in medical image analysis, they focus mainly in tissues and organs and none of the surveys about image cells consider an analysis following the stages in the typical image processing: Segmentation, feature extraction and classification. The goal of this study is to provide comprehensive and critical analyses about the trends in each stage of cell image processing. In this paper, we present a literature survey about cell identification using different image processing techniques.

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Section: Segmentationmentioning

confidence: 99%

Section: Texturementioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

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Study of Image Analysis Algorithms for Segmentation, Feature Extraction and Classification of Cells

Gamarra

Zurek

San-Juan

2017

Journal of Information Systems Engineering & Management

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“…The generated feature set encodes numerous important and relevant characteristics and distinctive properties of raw data that help in differentiating between the categories of input patterns. To represent such characteristics for HEp-2 cell images, various automated methods have been developed which typically compute large number of standard image-based features (e.g., morphology, texture and other sophisticated features like (Scale-invariant feature transform (SIFT) [11], Stein's unbiased risk estimate (SURE) [11], Local Binary Pattern (LBP) [12], Histogram of oriented gradients (HOG) etc. ).…”

Section: Introductionmentioning

confidence: 99%

Feature Importance for Human Epithelial (HEp-2) Cell Image Classification

Gupta

Bhavsar

2018

J. Imaging

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Indirect Immuno-Fluorescence (IIF) microscopy imaging of human epithelial (HEp-2) cells is a popular method for diagnosing autoimmune diseases. Considering large data volumes, computer-aided diagnosis (CAD) systems, based on image-based classification, can help in terms of time, effort, and reliability of diagnosis. Such approaches are based on extracting some representative features from the images. This work explores the selection of the most distinctive features for HEp-2 cell images using various feature selection (FS) methods. Considering that there is no single universally optimal feature selection technique, we also propose hybridization of one class of FS methods (filter methods). Furthermore, the notion of variable importance for ranking features, provided by another type of approaches (embedded methods such as Random forest, Random uniform forest) is exploited to select a good subset of features from a large set, such that addition of new features does not increase classification accuracy. In this work, we have also, with great consideration, designed class-specific features to capture morphological visual traits of the cell patterns. We perform various experiments and discussions to demonstrate the effectiveness of FS methods along with proposed and a standard feature set. We achieve state-of-the-art performance even with small number of features, obtained after the feature selection.

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